Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

• URL: http://arxiv.org/abs/2308.13546v2
• Date: Mon, 25 Sep 2023 04:11:04 GMT
• Title: Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors
• Authors: Jingyun Huang, Rachel C. Amey, Mengting Liu, Chad E. Forbes
• Abstract summary: This study focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects.
• Score: 1.8925617030516924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.

Related papers

• Feature Estimation of Global Language Processing in EEG Using Attention Maps [5.173821279121835]
  This study introduces a novel approach to EEG feature estimation that utilizes the weights of deep learning models to explore this association. We demonstrate that attention maps generated from Vision Transformers and EEGNet effectively identify features that align with findings from prior studies. The application of Mel-Spectrogram with ViTs enhances the resolution of temporal and frequency-related EEG characteristics.
  arXiv  Detail & Related papers  (2024-09-27T22:52:31Z)
• Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review [1.8310098790941458]
  The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. The development of AI systems for discerning complex emotions poses a substantial challenge with significant implications for affective computing. incorporating physiological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) can notably enhance CERS.
  arXiv  Detail & Related papers  (2024-09-09T05:06:10Z)
• Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition [23.505616142198487]
  We develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition. The model learns universal latent representations of EEG signals through pre-training on large scale dataset. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks.
  arXiv  Detail & Related papers  (2024-05-28T14:31:11Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships [0.913755431537592]
  In this work, we incorporate topographical structures with neural processing techniques to perform regression. We propose several models that significantly outperform current state-of-the-art of this task in resting state and task-based recording settings. Overall, these results suggest that EEG electrode relationships are pivotal to retain information necessary for haemodynamical activity retrieval.
  arXiv  Detail & Related papers  (2022-03-07T15:51:36Z)
• Progressive Graph Convolution Network for EEG Emotion Recognition [35.08010382523394]
  Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions. In EEG emotion recognition, we can observe that clearer boundaries exist between coarse-grained emotions than those between fine-grained emotions. We propose a progressive graph convolution network (PGCN) for capturing this inherent characteristic in EEG emotional signals.
  arXiv  Detail & Related papers  (2021-12-14T03:30:13Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.